This paper presents a new approach for the estimation and inference of the regression parameters in a panel data model with interactive fixed effects. It relies on the assumption that the factor loadings can be expressed as an unknown smooth function of the time average of covariates plus an idiosyncratic error term. Compared to existing approaches, our estimator has a simple partial least squares form and does neither require iterative procedures nor the previous estimation of factors. We derive its asymptotic properties by finding out that the limiting distribution has a discontinuity, depending on the explanatory power of our basis functions which is expressed by the variance of the error of the factor loadings. As a result, the usual ``plug-in" methods based on estimates of the asymptotic covariance are only valid pointwise and may produce either over- or under-coverage probabilities. We show that uniformly valid inference can be achieved by using the cross-sectional bootstrap. A Monte Carlo study indicates good performance in terms of mean squared error. We apply our methodology to analyze the determinants of growth rates in OECD countries.

翻译：本文提出了一种针对具有交互固定效应的面板数据模型中回归参数估计与推断的新方法。该方法基于以下假设：因子载荷可表示为协变量时间平均值的未知平滑函数加上一个异质误差项。与现有方法相比，我们的估计量具有简单的偏最小二乘形式，既不需要迭代过程，也无需事先估计因子。通过发现极限分布存在不连续性（该不连续性取决于基函数解释力，即因子载荷误差方差），我们推导了其渐近性质。因此，基于渐近协方差估计的常规"插入法"仅能在逐点意义上有效，可能导致覆盖概率偏高或偏低。我们证明，采用截面自助法可实现一致有效的推断。蒙特卡洛模拟结果表明该方法在均方误差方面表现良好。我们将该技术应用于分析OECD国家经济增长率的决定因素。